Evaluating Students' Writing and Performance Using Cutting-Edge AI Technologies in Medical Education

Abstract

This paper proposes an AI-based medical school writing exam. The recommended procedure begins with student writing collection and continues with thorough preparation and tokenization to ensure accurate analysis. TF-IDF, mood analysis, and consistency scores assess pupils' writing skills. The recommended strategy outperforms existing methods in truth, readability, language, and critical thinking. Performance evaluations are more trustworthy and consistent, with lower standard errors and better mean scores, showing that AI can make complicated judgments. Teachers may also create tailored lesson plans using adaptive learning technologies to help students progress. Medical students learn better and develop speaking skills for future careers with this practice. The findings pave the way for medical education assessment research. They also demonstrate how AI technology may transform evaluations and improve student learning.

Ähnliche Arbeiten